In vivo 1H-magnetic resonance spectroscopy can detect metabolic changes in APP/PS1 mice after donepezil treatment

Investigating neural dysfunction with abnormal protein deposition in Alzheimer's disease through magnetic resonance spectroscopic imaging, plasma biomarkers, and positron emission tomography

In Alzheimer's disease (AD), aggregated abnormal proteins induce neuronal dysfunction. Despite the evidence supporting the association between tau proteins and brain atrophy, further studies are needed to explore their link to neuronal dysfunction in the human brain. To clarify the relationship between neuronal dysfunction and abnormal proteins in AD-affected brains, we conducted magnetic resonance spectroscopic imaging (MRSI) and assessed the neurofilament light chain plasma levels (NfL). We evaluated tau and amyloid-β depositions using standardized uptake value ratios (SUVRs) of florzolotau (18F) for tau and 11C-PiB for amyloid-β positron emission tomography in the same patients. Heatmaps were generated to visualize Z scores of glutamate to creatine (Glu/Cr) and N-acetylaspartate to creatine (NAA/Cr) ratios using data from healthy controls. In AD brains, Z score maps revealed reduced Glu/Cr and NAA/Cr ratios in the gray matter, particularly in the right dorsolateral prefrontal cortex (rDLPFC) and posterior cingulate cortex (PCC). Glu/Cr ratios were negatively correlated with florzolotau (18F) SUVRs in the PCC, and plasma NfL levels were elevated and negatively correlated with Glu/Cr (P = 0.040, r = -0.50) and NAA/Cr ratios (P = 0.003, r = -0.68) in the rDLPFC. This suggests that the abnormal tau proteins in AD-affected brains play a role in diminishing glutamate levels. Furthermore, neuronal dysfunction markers including Glu/tCr and NAA/tCr could potentially indicate favorable clinical outcomes. Using MRSI provided spatial information about neural dysfunction in AD, enabling the identification of vulnerabilities in the rDLPFC and PCC within the AD's pathological context.

Microstructural imaging and transcriptomics of the basal forebrain in first-episode psychosis

Cholinergic dysfunction has been implicated in the pathophysiology of psychosis and psychiatric disorders such as schizophrenia, depression, and bipolar disorder. The basal forebrain (BF) cholinergic nuclei, defined as cholinergic cell groups Ch1-3 and Ch4 (Nucleus Basalis of Meynert; NBM), provide extensive cholinergic projections to the rest of the brain. Here, we examined microstructural neuroimaging measures of the cholinergic nuclei in patients with untreated psychosis (~31 weeks of psychosis, <2 defined daily dose of antipsychotics) and used magnetic resonance spectroscopy (MRS) and transcriptomic data to support our findings. We used a cytoarchitectonic atlas of the BF to map the nuclei and obtained measures of myelin (quantitative T1, or qT1 as myelin surrogate) and microstructure (axial diffusion; AxD). In a clinical sample (n = 85; 29 healthy controls, 56 first-episode psychosis), we found significant correlations between qT1 of Ch1-3, left NBM and MRS-based dorsal anterior cingulate choline in healthy controls while this relationship was disrupted in FEP (p > 0.05). Case-control differences in qT1 and AxD were observed in the Ch1-3, with increased qT1 (reflecting reduced myelin content) and AxD (reflecting reduced axonal integrity). We found clinical correlates between left NBM qT1 with manic symptom severity, and AxD with negative symptom burden in FEP. Intracortical and subcortical myelin maps were derived and correlated with BF myelin. BF-cortical and BF-subcortical myelin correlations demonstrate known projection patterns from the BF. Using data from the Allen Human Brain Atlas, cholinergic nuclei showed significant enrichment for schizophrenia and depression-related genes. Cell-type specific enrichment indicated enrichment for cholinergic neuron markers as expected. Further relating the neuroimaging correlations to transcriptomics demonstrated links with cholinergic receptor genes and cell type markers of oligodendrocytes and cholinergic neurons, providing biological validity to the measures. These results provide genetic, neuroimaging, and clinical evidence for cholinergic dysfunction in schizophrenia.

Magnetic Resonance Imaging in Animal Models of Alzheimer's Disease Amyloidosis

Amyloid-beta (Aβ) plays an important role in the pathogenesis of Alzheimer's disease. Aberrant Aβ accumulation induces neuroinflammation, cerebrovascular alterations, and synaptic deficits, leading to cognitive impairment. Animal models recapitulating the Aβ pathology, such as transgenic, knock-in mouse and rat models, have facilitated the understanding of disease mechanisms and the development of therapeutics targeting Aβ. There is a rapid advance in high-field MRI in small animals. Versatile high-field magnetic resonance imaging (MRI) sequences, such as diffusion tensor imaging, arterial spin labeling, resting-state functional MRI, anatomical MRI, and MR spectroscopy, as well as contrast agents, have been developed for preclinical imaging in animal models. These tools have enabled high-resolution in vivo structural, functional, and molecular readouts with a whole-brain field of view. MRI has been used to visualize non-invasively the Aβ deposits, synaptic deficits, regional brain atrophy, impairment in white matter integrity, functional connectivity, and cerebrovascular and glymphatic system in animal models of Alzheimer's disease amyloidosis. Many of the readouts are translational toward clinical MRI applications in patients with Alzheimer's disease. In this review, we summarize the recent advances in MRI for visualizing the pathophysiology in amyloidosis animal models. We discuss the outstanding challenges in brain imaging using MRI in small animals and propose future outlook in visualizing Aβ-related alterations in the brains of animal models.

Plasma metabolomics in early Alzheimer's disease patients diagnosed with amyloid biomarker

An untargeted metabolomics study has been carried out using plasma samples from patients with Mild Cognitive Impairment due to Alzheimer's disease patients (MCI-AD, n = 29) and healthy people (n = 29)). They have been classified following the National Institute on Aging and Alzheimer's Association (NIA-AA) recommendations and cerebrospinal fluid biomarkers. The analytical method was based on liquid chromatography coupled to high resolution mass spectrometry. The data process from the corresponding metabolic profiles retained 1158 molecular features in positive and 424 in negative ionization mode. Differences between metabolomic profiles from MCI-AD patients and healthy participants were investigated using a penalized logistic regression analysis (ElasticNet), and being able to select automatically the most informative variables (53 molecular features). From the molecular features selected for the elastic net models, 16 variables were preliminarily identified by The Human Metabolome Database (amino acids, lipids…). However, only 4 of these variables were tentatively identified by MS/MS and all ions fragmentation modes, being choline the only confirmed metabolite. Regarding their metabolic pathways, they could be involved in cholinergic system, energy metabolism, amino acids and lipids pathways. To conclude, this is a reliable approach to early AD mechanisms, and choline has been identified as a promising AD diagnosis metabolite. SIGNIFICANCE: The untargeted analysis carried out in human plasma samples from early Alzheimer's disease patients and healthy individuals, and the use of sophisticated statistical tools, identified some metabolic pathways and plasma biomarkers. Preliminarily, cholinergic system, energy metabolism, and aminoacids and lipids pathways may be involved in early Alzheimer's disease development.

Application of donepezil increased collagen 1 expression in mesenchymal stroma cells of an ovine osteoporosis model

OBJECTIVES

Donepezil inhibits the acetylcholine degradation molecule acetylcholinesterase (AChE). Clinical studies reported that Alzheimer's disease (AD) patients with hip fractures had improved bone quality and better fracture healing if they were treated with AD medication donepezil. We asked whether mesenchymal stroma cells (MSC) from an osteoporosis sheep model treated with donepezil increased their proliferation rate and mRNA expression.

METHODS

Sheep were divided into 4 groups: a) untreated control group, b) sheep with bilateral ovariectomy (OVX), c) sheep with OVX and malnutrition, and d) sheep with OVX, malnutrition, and application of corticosteroid. After 8 months MSC were isolated of iliac crest biopsy, treated with donepezil, and AChE activity, proliferation rate, and mRNA expression were analyzed.

RESULTS

Application of donepezil resulted in a significant decrease of AChE activity. Inhibition of AChE did not lead to a significant increase in proliferation. Expression of the osteogenic marker osteocalcin was not regulated by donepezil while the mRNA concentration of collagen was increased.

CONCLUSION

AChE inhibition via donepezil resulted in an increased synthesis of osteoid which consists mainly of collagen. Thus, we suppose that increased acetylcholine levels through AChE inhibition do not support MSC proliferation but osteogenic activity probably combined with osteogenic differentiation.

NSCs promote hippocampal neurogenesis, metabolic changes and synaptogenesis in APP/PS1 transgenic mice

Adult neurogenesis and synaptic remodeling persist as a unique form of structural and functional plasticity in the hippocampal dentate gyrus (DG) and subventricular zone (SVZ) of the lateral ventricles due to the existence of neural stem cells (NSCs). Transplantation of NSCs may represent a promising approach for the recovery of neural circuits. Here, we aimed to examine effects of highly neuronal differentiation of NSCs transplantation on hippocampal neurogenesis, metabolic changes and synaptic formation in APP/PS1 mice. 12-month-old APP/PS1 mice were used for behavioral tests, immunohistochemistry, western blot, transmission electron microscopy and proton magnetic resonance spectroscopy (1H-MRS). The results showed that N-acetylaspartate (NAA) and Glutamate (Glu) levels were increased in the Tg-NSC mice compared with the Tg-PBS and Tg-AD mice 10 weeks after NSCs transplantation. NSC-induced an increase in expression of synaptophysin and postsynaptic protein-95, and the number of neurons with normal synapses was significantly increased in Tg-NSC mice. More doublecortin-, BrdU/NeuN- and Nestin-positive neurons were observed in the hippocampal DG and SVZ of the Tg-NSC mice. This is the first demonstration that engrafted NSCs with a high differentiation rate to neurons can enhance neurogenesis in a mouse model of AD and can be detected by 1H-MRS in vivo. It is suggested that engraft of NSCs can restore memory and promote endogenous neurogenesis and synaptic remodeling, moreover, 1H-MRS can detect metabolite changes in AD mice in vivo. The observed changes in NAA/creatine (Cr) and glutamate (Glu)/Cr may be correlated with newborn neurons and new synapse formation.

Role of σ1 Receptors in Learning and Memory and Alzheimer's Disease-Type Dementia

The present chapter will review the role of σ1 receptor in learning and memory and neuroprotection , against Alzheimer's type dementia. σ1 Receptor agonists have been tested in a variety of pharmacological and pathological models of learning impairments in rodents these last past 20 years. Their anti-amnesic effects have been explained by the wide-range modulatory role of σ1 receptors on Ca2+ mobilizations, neurotransmitter responses, and particularly glutamate and acetylcholine systems, and neurotrophic factors. Recent observations from genetic and pharmacological studies have shown that σ1 receptor can also be targeted in neurodegenerative diseases, and particularly Alzheimer's disease . Several compounds, acting partly through the σ1 receptor, have showed effective neuroprotection in transgenic mouse models of Alzheimer's disease . We will review the data and discuss the possible mechanisms of action, particularly focusing on oxidative stress and mitochondrial integrity, trophic factors and a novel hypothesis suggesting a functional interaction between the σ1 receptor and α7 nicotinic acetylcholine receptor. Finally, we will discuss the pharmacological peculiarities of non-selective σ1 receptor ligands, now developed as neuroprotectants in Alzheimer's disease , and positive modulators, recently described and that showed efficacy against learning and memory deficits.

Alterations in brain metabolism and function following administration of low-dose codeine phosphate: 1H-magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging studies

The aim of the present study was to identify alterations in brain function following administration of a single, low-dose of codeine phosphate in healthy volunteers using resting-state functional magnetic resonance imaging (fMRI). In addition, the metabolic changes in the two sides of the frontal lobe were identified using ¹H-magnetic resonance spectroscopy (¹H-MRS). A total of 20 right-handed healthy participants (10 males, 10 females) were evaluated, and a Signa HDx 1.5T MRI scanner was used for data acquisition. An echo planar imaging sequence was used for resting-state fMRI, whereas a point resolved spectroscopy sequence was used for ¹H-MRS. Regional Saturation Technique, Data Processing Assistant for Resting-State fMRI, and Statistical Parameter Mapping 8 were used to analyze the fMRI data. The ¹H-MRS data were analyzed using LCModel software. At 1 h after oral administration of codeine phosphate (1.0 mg/kg), the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity were altered in different brain areas. The choline content was significantly increased in the right and left frontal lobes following codeine phosphate administration (P=0.02 and P=0.03, respectively), whereas the inositol content was significantly decreased in the left frontal lobe (P=0.02). There was no change in the glutamic acid content in the frontal lobes. In conclusion, the functions of different brain regions can be affected by a single, low-dose administration of codeine phosphate. The alterations in metabolite content in the two frontal lobes may be associated with changes in brain function, whereas the ALFF in the globus pallidus may have an effect on codeine phosphate addiction. Finally, glutamic acid may be useful in the estimation of codeine dependence.

Donepezil regulates energy metabolism and favors bone mass accrual

The autonomous nervous system regulates bone mass through the sympathetic and parasympathetic arms. The sympathetic nervous system (SNS) favors bone loss whereas the parasympathetic nervous system (PNS) promotes bone mass accrual. Donepezil, a central-acting cholinergic agonist, has been shown to down-regulate SNS and up-regulate PNS signaling tones. Accordingly, we hypothesize that the use of donepezil could have beneficial effects in regulating bone mass. To test our hypothesis, two groups of healthy female mice were treated either with donepezil or saline. Differences in body metabolism and bone mass of the treated groups were compared. Body and visceral fat weights as well as serum leptin level were increased in donepezil-treated mice compared to control, suggesting that donepezil effects on SNS influenced metabolic activity. Donepezil-treated mice had better bone quality than controls due to a decrease in osteoclasts number. These results indicate that donepezil is able to affect whole body energy metabolism and favors bone mass in young female WT mice.

Magnetic Resonance Spectroscopy discriminates the response to microglial stimulation of wild type and Alzheimer's disease models

Microglia activation has emerged as a potential key factor in the pathogenesis of Alzheimer’s disease. Metabolite levels assessed by magnetic resonance spectroscopy (MRS) are used as markers of neuroinflammation in neurodegenerative diseases, but how they relate to microglial activation in health and chronic disease is incompletely understood. Using MRS, we monitored the brain metabolic response to lipopolysaccharides (LPS)-induced microglia activation in vivo in a transgenic mouse model of Alzheimer’s disease (APP/PS1) and healthy controls (wild-type (WT) littermates) over 4 hours. We assessed reactive gliosis by immunohistochemistry and correlated metabolic and histological measures. In WT mice, LPS induced a microglial phenotype consistent with activation, associated with a sustained increase in macromolecule and lipid levels (ML9). This effect was not seen in APP/PS1 mice, where LPS did not lead to a microglial response measured by histology, but induced a late increase in the putative inflammation marker myoinositol (mI) and metabolic changes in total creatine and taurine previously reported to be associated with amyloid load. We argue that ML9 and mI distinguish the response of WT and APP/PS1 mice to immune mediators. Lipid and macromolecule levels may represent a biomarker of activation of healthy microglia, while mI may not be a glial marker.

The use of magnetic resonance spectroscopy as a tool for the measurement of bi-hemispheric transcranial electric stimulation effects on primary motor cortex metabolism

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of neurological and psychiatric disorders such as stroke and depression. Yet, the mechanisms underlying its ability to modulate brain excitability to improve clinical symptoms remains poorly understood. To help improve this understanding, proton magnetic resonance spectroscopy ((1)H-MRS) can be used as it allows the in vivo quantification of brain metabolites such as γ-aminobutyric acid (GABA) and glutamate in a region-specific manner. In fact, a recent study demonstrated that (1)H-MRS is indeed a powerful means to better understand the effects of tDCS on neurotransmitter concentration. This article aims to describe the complete protocol for combining tDCS (NeuroConn MR compatible stimulator) with (1)H-MRS at 3 T using a MEGA-PRESS sequence. We will describe the impact of a protocol that has shown great promise for the treatment of motor dysfunctions after stroke, which consists of bilateral stimulation of primary motor cortices. Methodological factors to consider and possible modifications to the protocol are also discussed.

Treatment effects in a transgenic mouse model of Alzheimer's disease: a magnetic resonance spectroscopy study after passive immunization

Despite the enormous public health impact of Alzheimer's disease (AD), no disease-modifying treatment has yet been proven to be efficacious in humans. A rate-limiting step in the discovery of potential therapies for humans is the absence of efficient non-invasive methods of evaluating drugs in animal models of disease. Magnetic resonance spectroscopy (MRS) provides a non-invasive way to evaluate the animals at baseline, at the end of treatment, and serially to better understand treatment effects. In this study, MRS was assessed as potential outcome measure for detecting disease modification in a transgenic mouse model of AD. Passive immunization with two different antibodies, which have been previously shown to reduce plaque accumulation in transgenic AD mice, was used as intervention. Treatment effects were detected by MRS, and the most striking finding was attenuation of myo-inositol (mIns) increases in APP-PS1 mice with both treatments. Additionally, a dose-dependent effect was observed with one of the treatments for mIns. MRS appears to be a valid in vivo measure of anti-Aβ therapeutic efficacy in pre-clinical studies. Because it is noninvasive, and can detect treatment effects, use of MRS-based endpoints could substantially accelerate drug discovery.

Recent advances in the application of metabolomics to Alzheimer's Disease

The pathophysiological changes associated with Alzheimer's Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis.

Drug pipeline in neurodegeneration based on transgenic mice models of Alzheimer's disease

Alzheimer's disease (AD) is one of the most important neurodegenerative disorders, bringing about huge medical and social burden in the elderly worldwide. Many aspects of its pathogenesis have remained unclear and no effective treatment exists for it. Within the past 20 years, various mice models harboring AD-related human mutations have been produced. These models imitate diverse AD-related pathologies and have been used for basic and therapeutic investigations in AD. In this regard, there are a wide variety of preclinical trials of potential therapeutic modalities using AD mice models which are of paramount importance for future clinical trials and applications. This review summarizes more than 140 substances and treatment modalities being used in transgenic AD mice models from 2001 to 2011. We also discuss advantages and disadvantages of each model to be used in therapeutic development for AD.

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer’s disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer’s disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion

The suggested revision of the NINCDS-ADRDA criterion for the diagnosis of Alzheimer's disease (AD) includes at least one abnormal biomarker among magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF). We aimed to investigate if the combination of baseline MRI and CSF could enhance the classification of AD compared to using either alone and predict mild cognitive impairment (MCI) conversion at multiple future time points. 369 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) were included in the study (AD=96, MCI=162 and CTL=111). Freesurfer was used to generate regional subcortical volumes and cortical thickness measures. A total of 60 variables were used for orthogonal partial least squares to latent structures (OPLS) multivariate analysis (57 MRI measures and 3 CSF measures: Aβ(42), t-tau and p-tau). Combining MRI and CSF gave the best results for distinguishing AD vs. CTL. We found an accuracy of 91.8% for the combined model at baseline compared to 81.6% for CSF measures and 87.0% for MRI measures alone. The combined model also gave the best accuracy when distinguishing between MCI vs. CTL (77.6%) at baseline. MCI subjects who converted to AD by 12 and 18month follow-up were accurately predicted at baseline using an AD vs. CTL model (82.9% and 86.4% respectively), with lower prediction accuracies for those MCI subjects converting by 24 and 36month follow up (75.4% and 68.0% respectively). The overall prediction accuracies for converters and non-converters ranged from 58.6% to 66.4% at different time points. Combining MRI and CSF measures in a multivariate model at baseline gave better accuracy for discriminating between AD and CTL, between MCI and CTL and for predicting future conversion from MCI to AD, than using either MRI or CSF separately.

Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease

Background

Visual assessment rating scales for medial temporal lobe (MTL) atrophy have been used by neuroradiologists in clinical practice to aid the diagnosis of Alzheimer's disease (AD). Recently multivariate classification methods for magnetic resonance imaging (MRI) data have been suggested as alternative tools. If computerized methods are to be implemented in clinical practice they need to be as good as, or better than experienced neuroradiologists and carefully validated. The aims of this study were: (1) To compare the ability of MTL atrophy visual assessment rating scales, a multivariate MRI classification method and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). (2) To assess how well the three techniques perform when predicting future conversion from mild cognitive impairment (MCI) to AD.

Methods

High resolution sagittal 3D T1w MP-RAGE datasets were acquired from 75 AD patients, 101 subjects with MCI and 81 CTL from the multi-centre AddNeuroMed study. An automated analysis method was used to generate regional volume and regional cortical thickness measures, providing 57 variables for multivariate analysis (orthogonal partial least squares to latent structures using seven-fold cross-validation). Manual hippocampal measurements were also determined for each subject. Visual rating assessment of MTL atrophy was performed by an experienced neuroradiologist according to the approach of Scheltens et al.

Results

We found prediction accuracies for distinguishing between AD and CTL of 83% for multivariate classification, 81% for the visual rating assessments and 89% for manual measurements of total hippocampal volume. The three different techniques showed similar accuracy in predicting conversion from MCI to AD at one year follow-up.

Conclusion

Visual rating assessment of the MTL gave similar prediction accuracy to multivariate classification and manual hippocampal volumes. This suggests a potential future role for computerized methods as a complement to clinical assessment of AD.

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

'Too much good news' - are Alzheimer mouse models trying to tell us how to prevent, not cure, Alzheimer's disease?

Scores of compounds ameliorate cognitive deficits or neuropathology in transgenic mouse models of Alzheimer's disease (AD), yet these triumphs in mice have not translated into successful therapies for people. Why have studies in mice failed to predict results of human trials? We argue that most transgenic mouse 'models of AD' actually simulate the asymptomatic phase of the disease, and the results of interventional studies in these mice should be considered in the context of disease prevention. In addition, recent advances in imaging technology and biomarker discovery should aid in comparisons of mouse and human neurological status and, importantly, might allow us to predict better the response of people to drugs tested in mice.

Multivariate statistical mapping of spectroscopic imaging data

For magnetic resonance spectroscopic imaging studies of the brain, it is important to measure the distribution of metabolites in a regionally unbiased way; that is, without restrictions to a priori defined regions of interest. Since magnetic resonance spectroscopic imaging provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of magnetic resonance spectroscopic imaging for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues, but it has not been previously employed for spectroscopic imaging (SI) studies of brain. The aims of this study were to (1) develop and validate multivariate voxel-based statistical mapping for magnetic resonance spectroscopic imaging and (2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons.